Evaluating floodplain vegetation after valley-scale restoration with unsupervised classification of National Agriculture Imagery Program data in semi-arid environments

Abstract

Monitoring vegetation response to valley-scale floodplain restoration to evaluate effectiveness can be costly and time-consuming. We used publicly available National Agriculture Imagery Program (NAIP) data and commonly used ArcGIS software to assess land cover change over time at five study sites located in semi-arid environments of eastern Oregon and north-central California. Accuracy assessments of our unsupervised classifications were used to evaluate effectiveness. Overall accuracy across sites and years ranged from 64.2% to 89.2% with mean and median accuracy of 79.1% and 80.6%, respectively. Further, we compared our classifications with high-resolution uncrewed aerial systems (UAS)-based data collected in the same timeframe. Restored areas classified as dense vegetation were within 4% of the UAS study, water was within 6%, and post-restoration classifications of sparse vegetation and bare ground classes were within 6% and 4% of the UAS study, respectively. This comparison demonstrates that our unsupervised NAIP data classification of land cover change across entire valley-scale restoration projects can be used to monitor riparian vegetation change over time as accurately as UAS-based methods, but at lower cost. Additionally, our methods leverage existing fine-resolution, pre-restoration vegetation density data that were not collected as part of project planning.